Pullen, N. | bioRxiv Science

bioRxiv2018

14-3-3 proteins activate Pseudomonas exotoxins-S and -T by chaperoning a hydrophobic surface

Pseudomonas are a common cause of hospital acquired infections that may be lethal. ADP-ribosyltransferase activities of Pseudomonas exotoxin-S and -T depend on 14-3-3 proteins inside the host cell. By binding in the 14-3-3 phosphopeptide binding groove, a hydrophobic C-terminal helix of ExoS and ExoT has been thought to be crucial for their activation. However, crystal structures of the 14-3-3{beta}:ExoS and -ExoT complexes presented here reveal an extensive novel binding interface that is sufficient for complex formation and toxin activation. We show that C-terminally truncated ExoS ADP-ribosyltransferase domain lacking the hydrophobic binding motif is active when co-expressed with 14-3-3. Moreover, swapping the hydrophobic C-terminus with a fragment from Vibrio Vis toxin creates a 14-3-3 independent toxin that ADP-ribosylates known ExoS targets. Finally, we show that 14-3-3 stabilizes ExoS against thermal aggregation. Together, this indicates that 14-3-3 proteins activate exotoxin ADP-ribosyltransferase domains by chaperoning their hydrophobic surfaces independently of the hydrophobic C-terminal segment.\n\nShort summaryCrystal structures of Pseudomonas exotoxins-S and -T identify a novel hydrophobic interface with 14-3-3 proteins, and we show that 14-3-3 activates these toxins independent of their phosphopeptide groove binding C-termini, by preventing their aggregation.

biochemistry↗

bioRxiv2017

Leaf-GP: An Open and Automated Software Application for Measuring Growth Phenotypes for Arabidopsis and Wheat

BackgroundPlants demonstrate dynamic growth phenotypes that are determined by genetic and environmental factors. Phenotypic analysis of growth features over time is a key approach to understand how plants interact with environmental change as well as respond to different treatments. Although the importance of measuring dynamic growth traits is widely recognised, available open software tools are limited in terms of batch processing of image datasets, multiple trait analysis, software usability and cross-referencing results between experiments, making automated phenotypic analysis problematic.\n\nResultsHere, we present Leaf-GP (Growth Phenotypes), an easy-to-use and open software application that can be executed on different platforms. To facilitate diverse scientific user communities, we provide three versions of the software, including a graphic user interface (GUI) for personal computer (PC) users, a command-line interface for high-performance computer (HPC) users, and an interactive Jupyter Notebook (also known as the iPython Notebook) for computational biologists and computer scientists. The software is capable of extracting multiple growth traits automatically from large image datasets. We have utilised it in Arabidopsis thaliana and wheat (Triticum aestivum) growth studies at the Norwich Research Park (NRP, UK). By quantifying growth phenotypes over time, we are able to identify diverse plant growth patterns based on a variety of key growth-related phenotypes under varied experimental conditions.\n\nAs Leaf-GP has been evaluated with noisy image series acquired by different imaging devices and still produced reliable biologically relevant outputs, we believe that our automated analysis workflow and customised computer vision based feature extraction algorithms can facilitate a broader plant research community for their growth and development studies. Furthermore, because we implemented Leaf-GP based on open Python-based computer vision, image analysis and machine learning libraries, our software can not only contribute to biological research, but also exhibit how to utilise existing open numeric and scientific libraries (including Scikit-image, OpenCV, SciPy and Scikit-learn) to build sound plant phenomics analytic solutions, efficiently and effectively.\n\nConclusionsLeaf-GP is a comprehensive software application that provides three approaches to quantify multiple growth phenotypes from large image series. We demonstrate its usefulness and high accuracy based on two biological applications: (1) the quantification of growth traits for Arabidopsis genotypes under two temperature conditions; and (2) measuring wheat growth in the glasshouse over time. The software is easy-to-use and cross-platform, which can be executed on Mac OS, Windows and high-performance computing clusters (HPC), with open Python-based scientific libraries preinstalled. We share our modulated source code and executables (.exe for Windows; .app for Mac) together with this paper to serve the plant research community. The software, source code and experimental results are freely available at https://github.com/Crop-Phenomics-Group/Leaf-GP/releases.

plant biology↗

bioRxiv2017

Regulatory divergence of flowering time genes in the allopolyploid Brassica napus

Polyploidy is a recurrent feature of eukaryotic evolution and has been linked to increases in complexity, adaptive radiation and speciation. Within angiosperms, such events occur repeatedly in many plant lineages. We investigated the role of duplicated genes in the regulation of flowering in Brassica napus. This relatively young allotetraploid represents a snapshot of evolution and artificial selection in progress. In line with the gene balance hypothesis, we find preferential retention of expressed flowering time genes relative to the whole genome. Furthermore, gene expression dynamics across development reveal diverged regulation of many flowering time gene copies. This finding supports the concept of responsive backup circuits being key for the retention of duplicated genes. A case study of BnaTFL1 reveals differences in cis-regulatory elements downstream of these genes that could explain this divergence. Such differences in the regulatory dynamics of duplicated genes highlight the challenges for translating gene networks from model to more complex polyploid crop species.

plant biology↗